Smart Card Research Articles

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

Nitrogen enrichment stimulates nutrient cycling genes of rhizosphere soil bacteria in the Phoebe bournei young plantations

Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in terrestrial ecosystems, endangering the balance of N and phosphorus (P) in P-deficient subtropical forest soil. Despite the widely reported responses of the microbial community to simulated N deposition, there is limited understanding of how N deposition affects the rhizosphere soil processes by mediating functional genes and community compositions of soil bacteria. Here, five levels of simulated N deposition treatments (N0, 0 g m−2·yr−1; N1, 100 g m−2·yr−1; N2, 200 g m−2·yr−1; N3, 300 g m−2·yr−1; and N4, 400 g m−2·yr−1) were performed in a 10-year-old Phoebe bournei plantation. Quantitative microbial element cycling smart chip technology and 16S rRNA gene sequencing were employed to analyze functional gene compositions involved in carbon (C), N, and P cycling, as well as rhizosphere bacterial community composition. N deposition significantly influenced C cycling relative abundance of genes in the rhizosphere soil, especially those involved in C degradation. Low and moderate levels (100–300 g m−2·yr−1) of N deposition promoted the relative abundance of the C decomposition-related genes (e.g., amyA, abfA, pgu, chiA, cex, cdh, and glx), whereas high N deposition (400 g m−2·yr−1) suppressed enzyme (e.g., soil invertase, soil urease, and soil acid phosphatase) activities, affecting the C cycling processes in the rhizosphere. Simulated N deposition affected the functional genes associated with C, N, and P cycling by mediating soil pH and macronutrients. These findings provide new insights into the management of soil C sequestration in P. bournei young plantations as well as the regulation of C, N, and P cycling and microbial functions within ecosystems.

Comparative analysis of remote user authentication schemes based on external memory

Because of recent availability of remote services and resources, remote user authentication becomes an essential component of all digital environment. Remote user login authentication is the process of validating the identity of a user. Users present their credentials, such as username and password, as evidence of their identity. The most practical and effectively implemented remote user authentication scheme is the smart card-based one, however, because it is expensive to acquire and operate smart card facilities, users may find it challenging to employ smart card authentication schemes in remote environments. Mobility and simplicity of acquisition—especially for remote access—are the benefits of using this external memory. Therefore, this research performs comparative analysis of these remote user authentication schemes based external memory. The schemes are first evaluated for security features and performance in terms of computation cost. The results of analysis shown that Buhari et al.’s scheme has the highest security feature and is the only scheme that uses light-weight tamper resistance client file, followed the reviewed Kumari et al.’s scheme which is the only scheme that handles user privacy. The most efficient scheme is Buhari et al.’s scheme followed by Rhee et al.’s scheme.

The structure and formation of non-volatile memory cells of Superflash

Split-gate embedded Flash memory technology has been around for decades and has become the standard for a wide range of devices, such as microcontrollers and smart cards. Among the, due to a number of advantages, Silicon Storage Technology Super Flash non-volatile memory technology has become the most widespread. This article presents the results of a study of the memory cells structure, examines in detail the principle of their operation and the main technological stages of the production process of forming transistor structures.

Recovery of precious metals from aqua regia leachate of e-wastes using dithiocarbamate-modified cellulose and development of a novel kinetic model

The escalating demand and dwindling natural supplies of precious metals (PMs) necessitate innovative hydrometallurgical recovery approaches with rapid kinetics and reduced environmental impact. The current study investigated the sorption and recovery of PMs from electronic waste (e-waste) utilizing dithiocarbamate-modified cellulose (DMC) and its proline derivative with epoxy cross-linkage (DMC-Pro-Epo-6) in aqua regia (AR). The effects of various sorption parameters, such as sorbent dose in feed, HCl:HNO3 ratio, AR dilution times, etc., on PM recovery were explored. Equilibrium for AuIII and PtIV sorption by DMC and DMC-Pro-Epo6 was reached in less than an hour, which is significantly faster than most reported sorbents. The maximum sorption capacities in AR settings, calculated using the Langmuir equation, were 348.0 mg g−1 for AuIII and 34.7 mg g−1 for PtIV for DMC, while DMC-Pro-Epo6 achieved 350.3 mg g−1 for AuIII and 31.1 mg g−1 for PtIV. These capacities, particularly for the higher sorption capacity of AuIII by DMCs, are attributed to the relativistic effects of gold, soft–soft interactions, and strong sulfur binding. FT-IR, FE-SEM, and EDX analyses elucidated the chelation bonding and mechanisms involved in PM sorption onto DMCs. By utilizing AR-DMC integrated techniques, step-by-step protocols were established for Au recovery from diverse e-waste sources, such as smartphones and smart chip cards. The study also highlighted the importance of chemical pre-concentration of e-waste to remove base metals, facilitating efficient PM recovery. Finally, a key innovation of this work is introducing a new pseudo-mix order fractional model, based on the preference of active sites with fractional assumptions, which more accurately correlates the sorption kinetics observed in complex systems.

Travel pattern recognition of urban rail passengers based on spatiotemporal sequence similarity

Understanding passenger travel patterns is helpful for the allocation of passenger resources in urban rail transit. Based on rail transit smart card data, this paper proposes a method to identify travel patterns by modeling individual spatiotemporal sequences. First, all the stations visited by individual passengers are extracted, and the similarity of the stations is calculated by the frequency of inter-station travel, the distance between stations and the activity duration of the stations. The main spatial activity area of the individual is divided using a hierarchical clustering algorithm. Then, the spatiotemporal sequence is inferred based on the individual's travel order. The sequence is a set of discrete values that characterize the spatiotemporal state. PCA-KL and K-Means++ are used to extract the similarity sequence structure to identify the passenger travel pattern. Finally, the rail transit smart card data of Xi'an in a certain month is taken as an example to identify its passenger travel pattern. The results show that complex passenger flow has 5 travel modes, of which 3 typical modes are commuting travel in a macro sense, accounting for 79% of the passenger flow. It can be seen that the pattern recognition based on the similarity of individual spatiotemporal sequences in this paper fully reflects the particularity and versatility of the research method, and is highly operational for different cities.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

An Improved and Secured 3-Factor Authentication Scheme for WSN in IoT Based Applications

Wireless Sensor Networks (WSN) has top-notch capacity for numerous domains of utility due to the potential to sense and apprehend unattended environments. IoT is playing an increasingly more essential position in clever healthcare, smart transportation and elegant grid using WSN. Therefore, challenges like privacy and data security associated with IoT wireless sensor networks because of sensor node being captured easily, consume less computing energy and low storage. Designing a secure authentication scheme will enable secure communication in WSN. Here, we proposed a improved three-factor authentication scheme for WSN utilizing biometric, password and smart card withstand against security attacks. Subsequently, by comparing the security, functional and performance parameters of our proposed scheme to the other schemes, our scheme provides better and efficient one. Therefore, our scheme is fairly workout in real time IoT applications involved in WSN scenario.

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

Robust Face Recognition System Based on Deep Facial Feature Extraction and Machine Learning

Facial Recognition is a common challenge in research for security applications. Wide-ranging applications of this technology include biometrics, security data, access to controlled locations, smart cards, and surveillance systems. A practical approach to identifying individuals depends on factors such as partial face occlusion, illumination, age, expression, makeup, and poses. These complex face recognition variables affect most face recognition systems. In this paper, we propose a deep transfer learning for facial features based on the pose and illumination variations of celebrities’ faces in a free environment for face recognition. The new approach that combines deep learning methods for feature extraction with machine learning classification methods. Also, studies the performance of the pre-trained model (VGGFace2 with ResNet-50 model weights) for feature extraction with a Multilayer Perceptron Classifier (MLP), Decision Tree, and Bootstrap Aggregation (Bagging) to perform classification. The convolution neural network has lately achieved excellent advancement in facial recognition. The outcome indicated that VGGFace2 with MLP obtained more precision and provided the best results (Accuracy, F-measure, Recall, and Precision). The results for all metric for the proposed model is 99%. The benchmark for this model is a dataset of 105 people’s faces.

Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data

In this paper, a probabilistic topic modeling algorithm called Latent Dirichlet Allocation (LDA) is implemented to infer trip purposes from activity attributes revealed from smart card transit data in an unsupervised manner. While most literature focused on finding patterns for home and work activities, we further investigated non-home and non-work-related activities to detect patterns associated with them. Temporal attributes of activities are extracted from trip information recorded by Tehran subway’s automatic fare collection system. In addition, land-use data is also incorporated to further enhance spatial attributes for non-home/work activities. Various activity attributes such as start time, duration, and frequency in addition to land-use data are used to infer the activity purposes and patterns. We identified 14 different patterns related to non-commuting activities on the basis of both their temporal and spatial attributes including educational, recreational, commercial, and health and other service-related activity types. We investigated passengers’ activity pattern and behavior changes before and during COVID-19 pandemic by comparing the discovered patterns. For recreational patterns it is revealed that not only has the number of recreational patterns dropped, but also the duration of recreational activities decreased. Morning patterns of educational activities have also been eliminated and number of commercial activities was decreased during COVID-19. The proposed model demonstrates the ability to capture travel behavior changes for different disruptions using smart card transit data without performing costly and time consuming manual surveys which can be useful for authorties and decision makers.

Analyzing the reporting error of public transport trips in the Danish national travel survey using smart card data

AbstractHousehold travel surveys have been used for decades to collect individuals and households’ travel behavior. However, self-reported surveys are subject to recall bias, as respondents might struggle to recall and report their activities accurately. This study examines the time reporting error of public transit users in a nationwide household travel survey by matching, at the individual level, five consecutive years of data from two sources, namely the Danish national travel survey (TU) and the Danish smart card system (Rejsekort). Survey respondents are matched with travel cards from the Rejsekort data solely based on the respondents’ declared spatiotemporal travel behavior. Approximately, 70% of the respondents were successfully matched with Rejsekort travel cards. The findings reveal a median time reporting error of 11.34 min, with an Interquartile Range of 28.14 min. Furthermore, a statistical analysis was performed to explore the relationships between the survey respondents’ reporting error and their socio-economic and demographic characteristics. The results indicate that females and respondents with a fixed schedule are in general more accurate than males and respondents with a flexible schedule in reporting their times of travel. Moreover, trips reported during weekdays or via the internet displayed higher accuracies compared to trips reported during weekends and holidays or via telephone interviews. This disaggregated analysis provides valuable insights that could help in improving the design and analysis of travel surveys, as well accounting for reporting errors/biases in travel survey-based applications. Furthermore, it offers valuable insights underlying the psychology of travel recall by survey respondents.

Smarter but more unequal transport? How socioeconomic and digital inequalities hinder adoption of mobility apps in the Global South

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

Resilience of socio-technical transportation systems: A demand-driven community detection in human mobility structures

Existing scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes.

Tiny Security Hole: First-Order Vulnerability of Masked SEED and Its Countermeasure.

Side-channel analysis is a type of cryptanalysis that utilizes the physical leakage of a cryptographic device. An adversary exploits the relationship between a physical leakage and the secret intermediate value of an encryption algorithm. In order to prevent side-channel analysis, the masking method was proposed. Several masking methods of the ISO/IEC 18033-3 standard encryption algorithm SEED have been proposed, as the Korean financial IC (integrated circuit) card standard (CFIP.ST.FINIC-01-2021) mandates using a robust implementation of SEED as an encryption algorithm against side-channel analyses. However, vulnerabilities were reported, except for with only one masking method. This study proposes the first-order vulnerability of that masking method. That is, an adversary is able to perform a side-channel analysis with the same complexity as an unprotected implementation. In order to fix this vulnerability, we revise the masking method with negligible additional overhead. Its vulnerability and security are theoretically verified and experimentally demonstrated. The round key of the existing masking method is revealed with only 210 power consumption traces, while that of the proposed masking method is not disclosed with 10,000 traces.

Security authentication scheme based on chebyshev chaotic mapping for library network

With the rapid advancement of information technology, the security of digital libraries, as critical platforms for modern knowledge dissemination, has become increasingly significant. User identity authentication in digital libraries is pivotal to ensuring system security. Traditional authentication methods often have security vulnerabilities and cannot effectively prevent unauthorized access. Therefore, this paper proposes a security authentication scheme based on chebyshev chaotic mapping for library. It addresses security issues in identity authentication in digital libraries. The proposed scheme integrates smart card, password and biometric factor, leveraging the chebyshev chaotic mapping algorithm to achieve stringent user authentication and generate secure session keys, thereby effectively countering security threats. Additionally, the scheme employs physical unclonable function (PUF) to enhance the protection of stored confidential data. The security of this scheme is rigorously analyzed using the Scyther. Security analysis and performance experiments indicate that the scheme not only ensures security but also optimizes authentication efficiency.

SPA-GPT: General Pulse Tailor for Simple Power Analysis Based on Reinforcement Learning

In side-channel analysis of public-key algorithms, we usually classify operations based on the differences in power traces produced by different basic operations (such as modular square or modular multiplication) to recover secret information like private keys. The more accurate the segmentation of power traces, the higher the efficiency of their classification. There exist two commonly used methods: one is equidistant segmentation, which requires a fixed number of basic operations and similar trace lengths for each type of operation, leading to limited application scenarios; the other is peak-based segmentation, which relies on personal experience to configure parameters, resulting in insufficient flexibility and poor universality. In this paper, we propose an automated trace segmentation method based on reinforcement learning applicable to a wide range of common implementation of public-key algorithms. The introduction of reinforcement learning, which doesn’t need labels, into trace processing for side-channel analysis marks its debut in this field. Our method has good universality on the traces with varying segment lengths and differing peak heights. By using prioritized experience replay optimized Deep Q-Network algorithm, we reduce the required number of parameters to one, which is the key length. We also employ various techniques to improve the segmentation effectiveness, such as clustering algorithm and enveloped-based feature enhancement. We validate the effectiveness of the new method in nine scenarios involving hardware and software implementations of different public-key algorithms executed on diverse platforms such as microcontrollers, SAKURA-G, and smart cards. Specifically, one of these implementations is protected by time randomization countermeasures. Experimental results show that a basic version of our method can correctly segment most traces. The enhanced version is capable of reconstructing the sequence of operations during trace segmentation, achieving an accuracy rate of 100% for the majority of the traces. For traces that cannot be entirely restored, we utilize reward values of reinforcement learning to correct errors and achieve fully recovery. We also conducted comparative experiments with supervised seq2seq methods, revealing our approach’s 42% higher accuracy in operation recovery and 96% faster time efficiency. In addition, we applied our method to the post-quantum cryptography Kyber, and successfully recovered an intermediate value crucial for deriving the secret key. Besides, power traces collected from these devices have been uploaded as open databases, which are available for researchers engaged in public-key algorithms to conduct related experiments or verify our method.

تأثير طريقة التّقليد والتّحفيظ باستخدام البطاقة الذّكيّة في فهم المفردات العربيّة لدى طلاب مدرسة أندلوسيا الابتدائيّة الإسلاميّة تابان

The researcher found problems in understanding vocabulary among fourth-grade students at Andalusia Islamic Primary School in Tapan. There are many educational methods and means that can be creative, including the game. There are no attractive educational methods in teaching vocabulary. Students think that the Arabic language is difficult, boring, and less interesting. Objectives of this scientific thesis: 1) Determine the effect of the method of imitation and memorization using the smart card on understanding Arabic vocabulary among students in the fourth semester of Andalusia Islamic Primary School, Taban. 2) To determine the extent of the effect of the method of imitation and memorization using a smart card on understanding vocabulary among students at Andalusia Taban Primary School. In this study, the researcher used multiple choice pre-test and post-test data collection. Asymp.Sig. (2-tailed) 000 < 0.05. From the data obtained, it can be concluded that the research hypothesis is acceptable, meaning that there is a significant impact on the effect of the method of imitation and memorization using the smart card on understanding Arabic vocabulary among the students of Andalusia Islamic Primary School, Tapan.

Analyzing the transfer duration of public transport passengers using classification and regression tree-multiple-Cox proportional hazards (CART-Multi-Cox) model

ABSTRACT Transfer behavior is a critical factor influencing the travel efficiency of public transportation passengers. To address the potential group heterogeneity, the present work developed an integrated Classification and Regression Tree-Multiple-Cox Proportional Hazards (CART-Multi-Cox) model for transfer behavior analysis using smart card data in Shenzhen, China. Specifically, passengers are first grouped into different types based on transfer behavior features, and the influence of various independent variables on the transfer duration of different passenger groups is then examined. The results reveal that the proposed CART-Multi-Cox model is able to account for the heterogeneity effect and provides a deeper understanding about passengers’ transfer behavior and its underlying influencing mechanism. The findings offer valuable references for refined transfer behavior management and help enhancing the competitiveness of public transportation.

Lightweight Certificate-Less Anonymous Authentication Key Negotiation Scheme in the 5G Internet of Vehicles

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios.